publication . Article . Preprint . Other literature type . 2017

Spatial features of synaptic adaptation affecting learning performance

Berger, Damian L.; De Arcangelis, Lucilla; Herrmann, Hans J.;
Open Access English
  • Published: 01 Sep 2017
Abstract
Recent studies have proposed that the diffusion of messenger molecules, such as monoamines, can mediate the plastic adaptation of synapses in supervised learning of neural networks. Based on these findings we developed a model for neural learning, where the signal for plastic adaptation is assumed to propagate through the extracellular space. We investigate the conditions allowing learning of Boolean rules in a neural network. Even fully excitatory networks show very good learning performances. Moreover, the investigation of the plastic adaptation features optimizing the performance suggests that learning is very sensitive to the extent of the plastic adaptation...
Subjects
free text keywords: Article, Medicine, R, Science, Q, Quantitative Biology - Neurons and Cognition, Condensed Matter - Disordered Systems and Neural Networks, Computer Science - Learning, Computer Science - Neural and Evolutionary Computing, Excitatory postsynaptic potential, Neural learning, Supervised learning, Synapse, Artificial neural network, Computer science, Artificial intelligence, business.industry, business
29 references, page 1 of 2

Schultz, W, Dickinson, A. Neuronal coding of prediction errors. Annual review of neuroscience. 2000; 23: 473-500 [OpenAIRE] [PubMed] [DOI]

Keller, GB, Hahnloser, RH. Neural processing of auditory feedback during vocal practice in a songbird. Nature. 2009; 457: 187-190 [PubMed] [DOI]

Keller, GB, Bonhoeffer, T, Hübener, M. Sensorimotor mismatch signals in primary visual cortex of the behaving mouse. Neuron. 2012; 74: 809-815 [OpenAIRE] [PubMed] [DOI]

Williams, D, Hinton, G. Learning representations by back-propagating errors. Nature. 1986; 323: 533-536 [OpenAIRE] [DOI]

Crick, F. The recent excitement about neural networks. Nature. 1989; 337: 129-132 [OpenAIRE] [PubMed] [DOI]

6.Kolen, J. F. & Pollack, J. B. Backpropagation without weight transport. In Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on, vol. 3, 1375–1380 (IEEE, 1994).

7.Stork, D. G. Is backpropagation biologically plausible. In International Joint Conference on Neural Networks, vol. 2, 241–246 (IEEE Washington, DC, 1989).

O’Reilly, RC. Biologically plausible error-driven learning using local activation differences: The generalized recirculation algorithm. Neural computation. 1996; 8: 895-938 [OpenAIRE] [DOI]

Lillicrap, TP, Cownden, D, Tweed, DB, Akerman, CJ. Random synaptic feedback weights support error backpropagation for deep learning. Nature Communications. 2016; 7: 13276 [OpenAIRE] [PubMed] [DOI]

Maass, W, Natschläger, T, Markram, H. Real-time computing without stable states: A new framework for neural computation based on perturbations. Neural computation. 2002; 14: 2531-2560 [OpenAIRE] [PubMed] [DOI]

Jaeger, H. The “echo state” approach to analysing and training recurrent neural networks-with anerratum note. Bonn, Germany: German National Research Center for Information Technology GMD Technical Report. 2001; 148: 13

12.Jalalvand, A., Van Wallendael, G. & Van De Walle, R. Real-time reservoir computing network-based systems for detection tasks on visual contents. In 2015 7th International Conference on Computational Intelligence, Communication Systems and Networks.

Fuxe, K. The discovery of central monoamine neurons gave volume transmission to the wired brain. Progress in neurobiology. 2010; 90: 82-100 [OpenAIRE] [PubMed] [DOI]

Liu, F. Direct protein–protein coupling enables cross-talk between dopamine d5 and γ-aminobutyric acid a receptors. Nature. 2000; 403: 274-280 [OpenAIRE] [PubMed] [DOI]

Reynolds, JN, Wickens, JR. Dopamine-dependent plasticity of corticostriatal synapses. Neural Networks. 2002; 15: 507-521 [PubMed] [DOI]

29 references, page 1 of 2
Abstract
Recent studies have proposed that the diffusion of messenger molecules, such as monoamines, can mediate the plastic adaptation of synapses in supervised learning of neural networks. Based on these findings we developed a model for neural learning, where the signal for plastic adaptation is assumed to propagate through the extracellular space. We investigate the conditions allowing learning of Boolean rules in a neural network. Even fully excitatory networks show very good learning performances. Moreover, the investigation of the plastic adaptation features optimizing the performance suggests that learning is very sensitive to the extent of the plastic adaptation...
Subjects
free text keywords: Article, Medicine, R, Science, Q, Quantitative Biology - Neurons and Cognition, Condensed Matter - Disordered Systems and Neural Networks, Computer Science - Learning, Computer Science - Neural and Evolutionary Computing, Excitatory postsynaptic potential, Neural learning, Supervised learning, Synapse, Artificial neural network, Computer science, Artificial intelligence, business.industry, business
29 references, page 1 of 2

Schultz, W, Dickinson, A. Neuronal coding of prediction errors. Annual review of neuroscience. 2000; 23: 473-500 [OpenAIRE] [PubMed] [DOI]

Keller, GB, Hahnloser, RH. Neural processing of auditory feedback during vocal practice in a songbird. Nature. 2009; 457: 187-190 [PubMed] [DOI]

Keller, GB, Bonhoeffer, T, Hübener, M. Sensorimotor mismatch signals in primary visual cortex of the behaving mouse. Neuron. 2012; 74: 809-815 [OpenAIRE] [PubMed] [DOI]

Williams, D, Hinton, G. Learning representations by back-propagating errors. Nature. 1986; 323: 533-536 [OpenAIRE] [DOI]

Crick, F. The recent excitement about neural networks. Nature. 1989; 337: 129-132 [OpenAIRE] [PubMed] [DOI]

6.Kolen, J. F. & Pollack, J. B. Backpropagation without weight transport. In Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on, vol. 3, 1375–1380 (IEEE, 1994).

7.Stork, D. G. Is backpropagation biologically plausible. In International Joint Conference on Neural Networks, vol. 2, 241–246 (IEEE Washington, DC, 1989).

O’Reilly, RC. Biologically plausible error-driven learning using local activation differences: The generalized recirculation algorithm. Neural computation. 1996; 8: 895-938 [OpenAIRE] [DOI]

Lillicrap, TP, Cownden, D, Tweed, DB, Akerman, CJ. Random synaptic feedback weights support error backpropagation for deep learning. Nature Communications. 2016; 7: 13276 [OpenAIRE] [PubMed] [DOI]

Maass, W, Natschläger, T, Markram, H. Real-time computing without stable states: A new framework for neural computation based on perturbations. Neural computation. 2002; 14: 2531-2560 [OpenAIRE] [PubMed] [DOI]

Jaeger, H. The “echo state” approach to analysing and training recurrent neural networks-with anerratum note. Bonn, Germany: German National Research Center for Information Technology GMD Technical Report. 2001; 148: 13

12.Jalalvand, A., Van Wallendael, G. & Van De Walle, R. Real-time reservoir computing network-based systems for detection tasks on visual contents. In 2015 7th International Conference on Computational Intelligence, Communication Systems and Networks.

Fuxe, K. The discovery of central monoamine neurons gave volume transmission to the wired brain. Progress in neurobiology. 2010; 90: 82-100 [OpenAIRE] [PubMed] [DOI]

Liu, F. Direct protein–protein coupling enables cross-talk between dopamine d5 and γ-aminobutyric acid a receptors. Nature. 2000; 403: 274-280 [OpenAIRE] [PubMed] [DOI]

Reynolds, JN, Wickens, JR. Dopamine-dependent plasticity of corticostriatal synapses. Neural Networks. 2002; 15: 507-521 [PubMed] [DOI]

29 references, page 1 of 2
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publication . Article . Preprint . Other literature type . 2017

Spatial features of synaptic adaptation affecting learning performance

Berger, Damian L.; De Arcangelis, Lucilla; Herrmann, Hans J.;